Handbook of Artificial Intelligence Applications in Energy Systems: Practical Techniques and Case Studies is a practical, step-by-step guidebook for integrating AI into energy systems, from foundational theories to hands-on implementation strategies. This book begins with the foundational principles and history of AI in Energy Systems, including fundamental methods for data preprocessing. A variety of optimization and control strategies are introduced in detail, including advanced methods (Adam, RMSprop, AdaGrad); next, machine learning techniques, libraries, and implementation strategies are broken down in depth. Modeling methods, evaluation metrics, deployment, and interpretability are addressed, before deep learning neural networks from CNNs to GANs, and finally hardware and software requirements for deploying AI in energy systems, including GPU/TPU usage, distributed training, and data storage solution.
Packed with case studies, worked numerical examples, and practical tips for research and implementation in the area, the
Handbook of Artificial Intelligence Applications in Energy Systems: Practical Techniques and Case Studies brings the reader quickly up to speed with the practical opportunities of this critical technology in modern energy systems.
- Introduces foundational methods for AI use in energy systems, step by step
- Works through a host of case studies, practical tests, and numerical examples to bed in knowledge
- Includes practical tips for research and industry application, and access to code files, answer keys, and original data
